An integrated self-adhesive rubber asphalt highway crack repair and monitoring system

Abstract

This invention belongs to the field of road engineering technology, specifically relating to an integrated self-adhesive rubber asphalt asphalt crack repair and monitoring system. The system includes: a self-adhesive rubber asphalt composite material layer for filling cracks and absorbing stress; a flexible sensing network unit embedded within the layer to sense strain and temperature; a data acquisition and signal conditioning module; a self-powered energy capture system that utilizes ambient energy to power the system; an edge computing terminal for localized damage assessment; and a remote cloud management platform. This system improves bonding strength and low-temperature flexibility through material modification, and combines embedded sensing and self-powered technology to achieve a leap from passive filling to active sensing of the repair body. It solves the problems of difficult power supply in the field and lack of coordination in monitoring, enabling real-time feedback of the interface mechanical state, significantly improving the scientific nature and predictability of highway maintenance, and reducing overall operation and maintenance costs.

Description

Technical Field

[0001] This invention belongs to the field of road engineering technology, specifically relating to an integrated self-adhesive rubber asphalt asphalt highway crack repair and monitoring system. Background Technology

[0002] In the field of transportation infrastructure construction and maintenance, the stable operation of the highway network is a crucial foundation for ensuring economic and social development. Asphalt pavement, as a major form of highway construction, inevitably suffers damage to its structural integrity during long-term service due to the combined effects of environmental temperature, rainfall erosion, and traffic loads. Timely and effective damage management and condition assessment are not only essential for extending the service life of highways but also core means of ensuring transportation safety.

[0003] Crack repair and real-time monitoring technology for asphalt highways is an important component of the preventive maintenance system for road surfaces. This technology typically involves the application of polymer composite materials and the deployment of sensor networks. It aims to strengthen the structure of cracks by leveraging the viscoelastic recovery and interfacial adhesion of materials, while simultaneously using information sensing methods to acquire data on crack width changes, displacement rates, and environmental response parameters, thereby providing a scientific basis for maintenance decisions.

[0004] Current technologies for treating asphalt cracks still face numerous challenges. Conventional filler materials exhibit significant brittleness at low temperatures and insufficient bond strength with the existing pavement, leading to frequent detachment of the repaired area under vehicle dynamic loads and failing to achieve long-term sealing. Furthermore, traditional pavement monitoring methods rely heavily on discrete sensor installations, lacking a collaborative mechanism with the repair materials. This not only makes the construction process cumbersome but also makes the sensor signals susceptible to external noise interference, failing to accurately reflect the mechanical evolution of the repair interface. This situation of poor material performance and lack of monitoring dimensions directly limits the rapid response capabilities of highway maintenance departments to potential risks, easily accelerating the deterioration of pavement structures. Therefore, an integrated self-adhesive rubber asphalt highway crack repair and monitoring system is desired. Summary of the Invention

[0005] The purpose of this invention is to provide an integrated self-adhesive rubber asphalt asphalt road crack repair and monitoring system to solve the problems of high low-temperature brittleness of existing asphalt pavement crack repair materials, low bonding strength with old pavement, easy detachment of repaired parts, lack of synergy between monitoring methods and repair materials, and difficulty in real-time feedback of interface mechanical state.

[0006] The technical solution of this invention includes: a self-adhesive rubber asphalt composite material layer for filling and sealing cracks in asphalt roads, achieving stress absorption and flexible connection of the pavement structure; a flexible sensing network unit embedded inside the self-adhesive rubber asphalt composite material layer for sensing strain field intensity, displacement vector, and ambient temperature changes in the crack area; a data acquisition and signal conditioning module connected to the flexible sensing network unit via electrical connection for amplifying, filtering, and analog-to-digital conversion of the original sensing signals to generate digital characterization signals; a self-powered energy capture system deployed inside or on the surface of the asphalt pavement structure for capturing environmental mechanical or thermal energy and converting it into electrical energy to provide a continuous energy supply for the data acquisition and signal conditioning module; an edge computing terminal for receiving digital characterization signals and performing localized calculations based on a preset crack evolution assessment model to extract crack propagation rate, fatigue damage degree, and material debonding risk indicators; and a remote cloud management platform that interacts with the edge computing terminal via a wireless communication link for performing cross-temporal and spatial correlation analysis of monitoring data from multiple points and issuing maintenance decision instructions.

[0007] In one embodiment of the present invention, the self-adhesive rubber asphalt composite material layer is prepared by a shear emulsification process from base asphalt, polymer modifier, tackifying resin, activated rubber powder, anti-aging additives, and nano-reinforcing fillers. The base asphalt has a penetration grade of 70 or 90 and comprises 100 parts by weight. The polymer modifier is composed of a 2:1 mass ratio of styrene-butadiene styrene block copolymer and styrene-butadiene rubber, comprising 12 to 18 parts by weight, used to construct a highly elastic three-dimensional cross-linked network. The tackifying resin includes petroleum resin or rosin resin, comprising 5 to 10 parts by weight, used to improve the initial wettability and long-term peel strength of the material at complex interfaces. The activated rubber powder is obtained from waste tires through low-temperature cryogenic pulverization and desulfurization activation treatment, with a mesh size of 60 to 80 mesh, comprising 20 to 30 parts by weight, used to enhance the material's resistance to high-temperature permanent deformation. The anti-aging additives include antioxidants and ultraviolet absorbers, comprising 0.5 to 1.5 parts by weight. The nano-reinforcing filler is specifically nano-silica or montmorillonite, in parts by weight of 2 to 5, used to improve the cohesive strength and impermeability of the material.

[0008] Furthermore, the flexible sensing network unit includes distributed carbon nanotube composite conductive fiber sensors and fiber Bragg grating sensors. The carbon nanotube composite conductive fiber sensor uses thermoplastic polyurethane as a substrate, achieving the piezoresistive effect by constructing a continuous carbon nanotube conductive network on its surface. The tensile limit of this sensor is not less than 150%, and its elastic modulus matches the modulus of the self-adhesive rubber asphalt composite layer to ensure the synergistic deformation of both under load. The fiber Bragg grating sensor is encapsulated within a high-strength carbon fiber composite tube and positioned in the central region of the crack to compensate for spurious strain signals caused by temperature fluctuations and to provide high-precision static displacement monitoring data.

[0009] Furthermore, the data acquisition and signal conditioning module includes a preamplifier circuit, a bandpass filter, and a 16-bit high-precision analog-to-digital converter. The input impedance of the preamplifier circuit is no less than 10 megohms to reduce signal attenuation during transmission. The cutoff frequency of the bandpass filter is set from 0.5 Hz to 50 Hz based on the dynamic characteristics of the vehicle load to filter out noise signals caused by electromagnetic interference. The sampling frequency of the digital characterization signal is no less than 200 Hz to capture the transient dynamic response generated when the vehicle passes by.

[0010] Furthermore, the self-powered energy capture system includes a piezoelectric energy harvesting array and a thermoelectric generator. The piezoelectric energy harvesting array consists of multiple sets of lead zirconate titanate piezoelectric ceramic transducers, buried 5 to 8 centimeters below the tire tracks on the road surface, utilizing the vertical pressure generated by vehicle movement to produce pulsed electrical energy. The thermoelectric generator utilizes the temperature gradient between the asphalt pavement surface and base layer to generate a continuous direct current through the Seebeck effect. The captured electrical energy is stored in a solid-state supercapacitor via a rectifier circuit and a voltage regulator circuit, ensuring that the system can maintain continuous monitoring operation for at least 72 hours even in the absence of sunlight or under extremely low traffic conditions.

[0011] Furthermore, the edge computing terminal integrates a microcontroller unit and a wireless communication module. The microcontroller unit runs an adaptive signal processing program to extract features from the real-time monitored strain waveform, including peak value, trough value, pulse width, and residual strain increment. The wireless communication module supports narrowband IoT communication protocols or 5G mobile communication technology, enabling low-power, long-distance data transmission.

[0012] Furthermore, the crack evolution assessment model executed by the edge computing terminal includes the following steps: Step 1, acquiring the real-time strain sequence fed back by the flexible sensing network unit, and using the temperature data of the fiber Bragg grating sensor for temperature drift compensation; Step 2, calculating the mean change rate of the strain peak value per unit time. If the change rate exceeds the preset 10% threshold for three consecutive monitoring cycles, the crack is determined to have entered the accelerated propagation stage; Step 3, extracting the residual strain data after the vehicle load. If the residual strain shows a step-like growth trend, it is determined that local debonding has occurred between the self-adhesive rubber asphalt composite material layer and the crack interface; Step 4, generating a crack health index by comprehensively considering the strain amplitude, propagation rate, and degree of debonding, and dividing the index into four levels, corresponding to normal monitoring, preventive maintenance, structural reinforcement, and emergency repair.

[0013] Furthermore, the remote cloud management platform includes a geospatial information system (GIS) database and a deep learning prediction module. The GIS database stores coordinate information, construction records, material batches, and historical monitoring data for all crack repairs within the territory. The deep learning prediction module, based on a long short-term memory (LSTM) network structure, takes historical traffic flow, rainfall, extreme temperature changes, and real-time monitoring indicators as input, and outputs a pavement structure defect evolution prediction report for the next 30 to 90 days.

[0014] Furthermore, the system's construction process includes crack pretreatment, sensor unit deployment, material filling, and protective sealing. Crack pretreatment employs high-pressure air dust removal and hot air drying to ensure the internal dryness of the crack is no less than 95%. During sensor unit deployment, a 1-2 mm thick layer of self-adhesive rubber asphalt is first applied to the bottom of the crack as a leveling layer, followed by tensioning and fixing the flexible sensor network units according to a pre-set orientation. During material filling, the melting temperature is maintained between 160 and 180 degrees Celsius, using a layered pouring process, with each layer's thickness controlled to within 10 mm. In the protective sealing stage, a layer of modified asphalt anti-crack tape is applied to the repaired area, and fine sand is sprinkled to prevent wheel adhesion during the initial traffic period.

[0015] In one embodiment of the present invention, the self-adhesive rubber asphalt composite material layer exhibits the following adhesion properties: a penetration of 1 / 50 to 1 / 70 mm at 25 degrees Celsius, a softening point of not less than 85 degrees Celsius, and a ductility of not less than 30 cm at 5 degrees Celsius. This combination of parameters ensures that the material does not flow or stick to the wheel in high-temperature summer environments, and does not become brittle in low-temperature winter environments, maintaining a tight adhesion to the sidewalls of cracks at all times.

[0016] Furthermore, the carbon nanotube composite conductive fiber sensors in the flexible sensing network unit are arranged in an interwoven mesh structure, with the mesh density determined according to the highway technical grade. For Class I highways and expressways, the mesh spacing is set to 5 cm; for Class II and III highways, the mesh spacing is set to 10 cm. This mesh layout achieves full coverage monitoring of stress distribution in crack areas, effectively avoiding the risk of missed detection that may exist with point sensors.

[0017] Furthermore, the data acquisition and signal conditioning module also integrates a self-diagnostic unit for periodically checking the impedance status of the sensor link. When the impedance value deviates from the initial calibration value by more than 30%, the system automatically identifies it as sensor damage or lead breakage and generates a hardware fault alarm on the remote cloud management platform to avoid erroneous data misleading maintenance decisions.

[0018] Furthermore, the supercapacitors in the self-powered energy capture system adopt a parallel redundant design with a total capacity of no less than 50 farads. The charge and discharge management circuit has overcharge and over-discharge protection functions, and its static power consumption is controlled below 10 microamps, maximizing the utilization efficiency of environmental energy.

[0019] Furthermore, the microcontroller unit of the edge computing terminal adopts a dual-core architecture. The first core is responsible for high-frequency data acquisition and real-time processing, while the second core is responsible for maintaining the wireless communication protocol stack and scheduling low-frequency tasks. The two cores exchange data through shared memory, ensuring the real-time performance of monitoring tasks and the stability of communication tasks.

[0020] Furthermore, the remote cloud management platform also features 3D visualization capabilities. Based on a high-precision road network model, the platform can dynamically display the stress cloud map of cracks across the entire road section. When the strain intensity in a certain area exceeds the material's design allowable stress value, the corresponding road section on the platform interface will change from green to flashing red, and a warning message will be simultaneously pushed to the mobile terminals of maintenance personnel.

[0021] Furthermore, the system also includes a handheld calibration terminal for initial zeroing and gain calibration of the sensor signals at the construction site. The handheld calibration terminal establishes a short-range connection with the edge computing terminal via Bluetooth, reads the raw level of the sensor under no-load conditions, and stores it as a reference bias value, which serves as the physical origin for all subsequent calculations.

[0022] Furthermore, in the preparation process of the self-adhesive rubber asphalt composite layer, the rotation speed of shear emulsification is controlled at 3000 to 5000 rpm, and the duration is not less than 45 minutes. This process parameter ensures that the polymer modifier and activated rubber powder are fully swollen and uniformly dispersed in the matrix asphalt, eliminating the risk of macroscopic phase separation.

[0023] Furthermore, the system's monitoring frequency has adaptive adjustment characteristics. When the edge computing terminal detects that the ambient temperature is in a freezing or high-temperature period, or when traffic flow increases dramatically, the system automatically increases the sampling frequency to 400 Hz; during periods of mild ambient temperature and low traffic flow, the system automatically switches to a low-power sleep mode, reducing the sampling frequency to 10 Hz, in order to extend the overall service life of the system.

[0024] Furthermore, when repairing wide cracks, i.e. cracks wider than 20 mm, the system incorporates 3 to 5 parts by weight of polypropylene fibers with a fiber length of 12 to 15 mm into the self-adhesive rubber asphalt composite material layer. The bridging effect of the fibers limits the secondary cracking of the crack and provides additional physical protection for the flexible sensing network unit.

[0025] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0026] This invention develops a self-adhesive rubber asphalt composite material with a specific ratio. By utilizing multiple synergistic modifications of styrene-butadiene-styrene block copolymer, styrene-butadiene rubber, and activated rubber powder, a viscoelastic system with high toughness and high adhesion is constructed. This material exhibits excellent flexibility at low temperatures, effectively overcoming the drawbacks of traditional repair materials that are prone to brittleness. Its bonding strength with the old pavement interface is increased by more than 40% compared with traditional materials, ensuring the long-term service stability of the repaired area under heavy traffic loads.

[0027] This invention achieves a transformation of the repair body from passive filling to active sensing by directly integrating flexible sensor network units inside the repair material; the high degree of matching between the sensor and the asphalt matrix in terms of mechanical parameters eliminates the sudden change in interface stiffness between traditional embedded sensors and the road structure, and can truly collect microscopic mechanical evolution data of the deep cracks, solving the problem that the sensor signal is easily interfered with and cannot reflect the true stress distribution.

[0028] This invention integrates a self-powered energy capture system with an edge computing terminal to build a completely closed-loop autonomous monitoring ecosystem. Through piezoelectric and thermoelectric dual-energy complementary capture technology, it completely eliminates the dependence of highway field monitoring stations on mains power or frequent battery replacements. Edge computing technology completes feature extraction of massive information at the data source, significantly reducing the bandwidth pressure and power consumption of wireless communication, enabling the system to have a second-level response capability to the risk of sudden deterioration of cracks.

[0029] The systematic framework established by this invention, encompassing materials science, electronic sensing, energy harvesting, and cloud-based big data, provides information support for the entire lifecycle of highway maintenance. The deep learning prediction function of the cloud platform transforms the maintenance model from the existing reactive repair to scientific preventive maintenance, accurately predicting potential structural deterioration risks, thereby rationally allocating maintenance resources, significantly reducing the overall operation and maintenance costs of the highway network, and extending the service life of asphalt pavements. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the overall technical solution architecture proposed in this invention;

[0031] Figure 2 This is a schematic diagram of the core principle framework of the collaborative sensing between the flexible sensing network unit and the self-adhesive rubber asphalt composite material layer in this invention;

[0032] Figure 3 This is a logical flowchart of the crack health index evaluation and evolution assessment stage in this invention;

[0033] Figure 4 This is a schematic diagram of the multi-level interaction relationship and data flow between the edge computing terminal and the remote cloud management platform in this invention;

[0034] Figure 5 This is a schematic diagram of the core principle framework of the self-powered energy capture system in this invention for energy capture and conversion. Detailed Implementation

[0035] Example 1

[0036] Please refer to the appendix. Figure 1 This embodiment provides an integrated self-adhesive rubber asphalt system for repairing and monitoring cracks in asphalt roads. This system is built upon a framework that deeply integrates novel viscoelastic materials science and Internet of Things (IoT) sensing technology. The core of the system lies in the self-adhesive rubber asphalt composite material layer, which not only serves as the structural main body for filling and sealing cracks in asphalt roads but also as a medium for stress absorption and flexible connection. (See attached...) Figure 2The self-adhesive rubber asphalt composite material layer incorporates a flexible sensor network unit. This unit senses strain field intensity, displacement vector, and changes in ambient temperature within the crack area, converting the physical evolution of the damage into electrical signals. To achieve closed-loop data processing, the system includes a data acquisition and signal conditioning module, electrically connected to the flexible sensor network unit, responsible for conditioning and digitizing the raw sensor signals. The system's energy is supplied by a self-powered energy capture system deployed within or on the surface of the asphalt pavement structure, converting mechanical or thermal energy from the environment into stable electrical energy. At the data processing level, the edge computing terminal receives digitized representation signals from the data acquisition and signal conditioning module and performs localized calculations using built-in algorithm models to extract key structural health indicators. Finally, the remote cloud management platform interacts with the edge computing terminal via a wireless communication link to complete collaborative analysis and decision-making for the large-scale road network.

[0037] The self-adhesive rubber asphalt composite layer is the foundation for the entire system's repair function. Its formulation is precisely designed to ensure long-term service performance under extreme temperature environments. Specifically, the composite layer consists of base asphalt, polymer modifier, tackifying resin, activated rubber powder, anti-aging additives, and nano-reinforcing fillers. The base asphalt is high-quality petroleum asphalt with a penetration grade of 70 or 90, and its mass fraction is set at 100 parts. The polymer modifier, serving as the core reinforcing phase, is a mixture of styrene-butadiene styrene block copolymer and styrene-butadiene rubber at a mass ratio of 2:1, with a total mass fraction of 15 parts. This combination utilizes the thermoplastic elastomer properties of the styrene-butadiene styrene block copolymer to construct a highly elastic three-dimensional cross-linked network within the asphalt, significantly improving the material's resistance to fatigue crack propagation; while the styrene-butadiene rubber provides excellent low-temperature elongation, ensuring that the material does not become brittle at -30 degrees Celsius. The tackifying resin, selected from C5 petroleum resin, comprises 8 parts by weight. Its polar groups significantly enhance the initial wettability of the material at wet or dusty crack interfaces, resulting in a substantial increase in the peel strength between the material and the old pavement. The activated rubber powder, made from waste tires, is cryogenically pulverized to 80 mesh and then desulfurized and activated, comprising 25 parts by weight. These fine rubber particles are distributed within the asphalt matrix, enhancing the material's resistance to permanent deformation at temperatures above 60 degrees Celsius through a combination of physical filling and chemical bonding. The anti-aging additives include 1 part by weight of hindered amine antioxidants and benzotriazole UV absorbers, effectively extending the oxidation induction period of the material under UV irradiation. The nano-reinforcing filler, made from nano-silica, comprises 4 parts by weight. Its large specific surface area significantly improves the cohesive strength of the asphalt system and enhances the pavement structure's resistance to water seepage.

[0038] In terms of manufacturing process, the self-adhesive rubber-asphalt composite layer is prepared through a shear emulsification process. The base asphalt is heated to 175 degrees Celsius, followed by the addition of a polymer modifier and activated rubber powder. The rotation speed of the shear emulsifier is strictly controlled at 4500 rpm, with a continuous shearing time of 60 minutes. Under this high shear force, the long polymer chains swell and are uniformly anchored within the asphalt molecular clusters. The active groups on the surface of the activated rubber powder undergo an interfacial reaction with the asphalt components, forming a macroscopically homogeneous viscoelastic system without phase separation. The final material exhibits a penetration of 1 / 60th of a millimeter at 25 degrees Celsius, a softening point of 92 degrees Celsius, and a stable ductility of 35 centimeters at 5 degrees Celsius, demonstrating extremely high viscoelastic equilibrium.

[0039] Combined with appendix Figure 2 The flexible sensing network unit is the core component of this system, enabling its sensing function. This unit comprises distributed carbon nanotube composite conductive fiber sensors and fiber Bragg grating sensors. The carbon nanotube composite conductive fiber sensor uses thermoplastic polyurethane as the elastic substrate, constructing a continuous and dense carbon nanotube conductive network on the substrate surface through an impregnation coating process. This sensor utilizes the piezoresistive effect for strain sensing; that is, when the material is stretched or compressed, the contact resistance between the carbon nanotube networks changes linearly or exponentially. Experimental tests show that the sensor's tensile limit reaches 180%, completely following the deformation of the self-adhesive rubber asphalt composite layer without physical damage. The fiber Bragg grating sensor is encapsulated within a 2 mm diameter high-strength carbon fiber composite tube and positioned in the central region of the crack. Because the fiber Bragg grating is sensitive to both temperature and strain, the system uses it as a temperature reference to compensate for the resistance drift of the carbon nanotube sensor caused by temperature fluctuations in real time. The sensor layout presents a mesh structure; for highways, the grid spacing is set to 5 cm. This high-density arrangement can capture subtle changes in stress concentration at the crack tip.

[0040] The data acquisition and signal conditioning module, acting as a bridge between the flexible sensor network unit and the digital system, integrates a high-precision analog front-end. The preamplifier circuit employs an instrumentation amplifier architecture with an input impedance designed to be 20 megohms, effectively addressing the signal attenuation and impedance matching issues caused by long-distance transmission in flexible sensors. The bandpass filter's cutoff frequency range is set between 0.5 Hz and 50 Hz, covering the dynamic response frequencies generated by vehicles ranging from heavy trucks to small buses, while filtering out 50 Hz power frequency interference introduced by road electrical infrastructure. A 16-bit high-precision analog-to-digital converter quantizes the analog signal at a sampling frequency of 200 Hz, generating a digital characterization signal that clearly depicts the rising and falling edges of the strain waveform under vehicle load.

[0041] Combined with appendix Figure 5The self-powered energy capture system solves the energy bottleneck for long-term monitoring in field environments. The piezoelectric energy harvesting array consists of 24 sets of lead zirconate titanate piezoelectric ceramic transducers, each using a full-bridge rectifier circuit. These arrays are buried 6 centimeters below the tire tracks on the road surface. When a vehicle passes, the vertical pressure generated by the road surface acts on the transducers, producing a pulse current with a peak instantaneous voltage of up to 40 volts. The thermoelectric generator utilizes the temperature gradient between the high temperature of the asphalt surface under sunlight and the low temperature of the underlying soil, with a temperature difference of up to 25 degrees Celsius. Through the Seebeck effect, the thermoelectric plate generates a constant direct current. All captured electrical energy is collected in a 50-farad solid-state supercapacitor. This capacitor has an ultra-long cycle life, and combined with a charge / discharge management circuit with a static power consumption of only 8 microamps, it ensures that the system can maintain full-load operation for at least 72 hours even at night with no traffic or during continuous rainy weather.

[0042] The edge computing terminal integrates a dual-core microcontroller unit. The first core is dedicated to running a high-speed sampling program and an adaptive signal processing algorithm. This algorithm can automatically identify vehicle passage events from a chaotic strain sequence and extract strain peaks, residual strain increments, and load application time. The second core manages the wireless communication protocol stack based on the NB-IoT protocol. The crack evolution assessment model running inside the edge computing terminal is crucial for determining road surface health. Its calculation logic is as follows: Step 1: Acquire the real-time strain sequence fed back by the flexible sensor network unit and use temperature data from the fiber Bragg grating sensor for temperature drift compensation to obtain the net strain value. Step 2: Evaluate the dynamic propagation performance of the crack by calculating the average rate of change of the strain peak value per unit time. If this rate of change exceeds a preset threshold of 10% for three consecutive monitoring periods, the edge computing terminal immediately triggers an accelerated propagation warning. Step 3: Extract the residual strain after load unloading. If the residual strain exhibits a step-like growth characteristic, it indicates that micro-cracks or debonding have occurred between the self-adhesive rubber asphalt composite layer and the crack sidewall, and the interfacial bonding performance is deteriorating. Step 4: Combining the above indicators, the model calculates the crack health index using the following formula:

[0043]

[0044] In the above formula, This represents the crack health index, with a value ranging from 0 to 1. This indicates the maximum strain peak value detected in real time. This indicates the design limit allowable strain for self-adhesive rubber asphalt materials. This represents the residual strain increment within a continuous monitoring period. This represents the cumulative total strain. This indicates the current crack propagation rate. This indicates the preset reference rate index. , , These are weighting coefficients, calibrated based on road surface grade and traffic volume distribution. When the H-index is above 0.8, the system determines it to be in an emergency repair state.

[0045] Combined with appendix Figure 4 The remote cloud management platform receives compressed data packets transmitted from edge computing terminals across the country. The platform's internal geographic information system database stores the precise latitude and longitude coordinates and maintenance history of each monitoring point. The deep learning prediction module, based on a long short-term memory network structure, models historical strain trends. The prediction module considers not only the direct output of road surface sensors but also accesses real-time rainfall and light intensity data from the meteorological bureau via an interface. Through multi-dimensional spatiotemporal correlation analysis, the platform can predict whether cracks will cause penetrating damage within the next 60 days. The platform also provides a 3D visualization interface, transforming complex strain data into an intuitive road surface stress cloud map. When the health index reaches a threshold, the cloud automatically sends mobile instructions to the maintenance manager in the corresponding jurisdiction.

[0046] The system implementation process includes strict procedure control. In the crack pretreatment stage, high-pressure air at 1.0 MPa is used to thoroughly remove loose particles and accumulated water from the cracks. Subsequently, a hot air gun is used to dry the sidewalls, ensuring a dryness level of over 98%. In the sensor unit deployment stage, a 1.5 mm thick self-adhesive rubber asphalt leveling layer is first laid at the bottom of the crack. The material temperature is maintained at 160 degrees Celsius to ensure good fluidity and to encapsulate the bottom of the sensor. The flexible sensor network unit is fixed to the leveling layer under tension, followed by layered crack filling. Each layer is no more than 10 mm thick, and the next layer is poured only after the previous layer has cooled to 60 degrees Celsius. This process effectively prevents voids caused by thermal stress concentration within the material.

[0047] Example 2

[0048] In this embodiment, a targeted enhancement configuration was implemented for the repair of wide cracks in primary highways and expressways, specifically for asphalt road crack repair and monitoring systems integrating self-adhesive rubber asphalt. When the crack width exceeds 20 mm, the mechanical stability of the self-adhesive rubber asphalt composite layer faces greater challenges. Therefore, four parts by weight of polypropylene fibers were additionally introduced into the material formulation. These fibers, with lengths ranging from 12 mm to 15 mm, form a randomly distributed reinforcing system within the base asphalt through physical overlap. The introduction of polypropylene fibers not only provides a significant bridging effect, limiting secondary cracking under heavy vehicle loads, but also provides an additional physical barrier for the flexible sensing network unit, preventing thermal shock damage to sensitive elements from high-temperature asphalt during crack filling.

[0049] Regarding monitoring frequency configuration, this embodiment introduces an adaptive frequency adjustment strategy. The edge computing terminal acquires the road surface temperature in real time through built-in environmental sensors and makes logical judgments based on the frequency sensed by the load. When the road surface temperature exceeds 55 degrees Celsius at noon in summer, or when an extreme cold wave in winter causes the temperature to drop below -15 degrees Celsius, the system automatically increases the sampling frequency to 400 Hz because the modulus of the asphalt material will change drastically. This high-frequency sampling can capture the nonlinear creep process of the material under extreme environments. Conversely, during the off-peak traffic periods in spring or autumn, the system automatically enters an ultra-low power mode, reducing the sampling frequency to 10 Hz and maintaining only basic connectivity heartbeat packet transmission. At this time, the energy stored in the self-powered energy capture system will be prioritized for the self-balancing maintenance of the supercapacitor.

[0050] For the arrangement of flexible sensing network units in large-span cracks, this embodiment adopts a double-layer composite structure. In addition to the bottom carbon nanotube composite conductive fiber sensor, a metal woven shielding mesh is added to the upper middle part of the composite material layer. This mesh not only plays a role in sharing tensile stress, but also effectively shields the electromagnetic radiation generated by the high-voltage transmission lines above the highway, thereby improving the signal-to-noise ratio of the data acquisition and signal conditioning module by 15 dB.

[0051] To ensure long-term monitoring accuracy, this embodiment of the system is equipped with a handheld calibration terminal. This terminal establishes a connection with the edge computing terminal at the construction site using Bluetooth. After the material pouring is completed and cooled to room temperature, maintenance personnel use the handheld calibration terminal to perform initial zeroing of the sensor. This process involves reading the original voltage level under no-load conditions and writing it into the non-volatile memory of the edge computing terminal as the physical origin. All subsequent strain calculations are performed differentially based on this reference bias value, thereby eliminating the influence of initial residual stress generated during sensor installation on the monitoring results.

[0052] In terms of data processing algorithms, the remote cloud management platform incorporates predictive logic based on fatigue damage mechanics. The platform assesses the remaining service life of the repaired area using the following formula:

[0053]

[0054] In this formula, This represents the predicted remaining service life. This indicates the cumulative number of times the axle load has been applied. Indicates the first The amplitude of cyclic strain generated by secondary load. It is a material constant related to the viscoelastic characteristics of self-adhesive rubber asphalt. It is a correction function that takes into account environmental factors, humidity fluctuations, and material aging rates. Using this prediction formula, the cloud platform can predict the structural integrity of the repaired area within the next quarter with a confidence level of over 90%, thereby providing management with an accurate preventative maintenance schedule.

[0055] Example 3

[0056] This embodiment focuses on the application performance of the system under high-frequency load conditions on urban expressways. Due to the extremely high traffic volume and proportion of heavy vehicles on urban expressways, the rutting resistance of the self-adhesive rubber-asphalt composite layer becomes a key concern. In this scenario, the mass fraction of activated rubber powder was adjusted to 30 parts, and 3 parts of nano-montmorillonite were introduced as an auxiliary reinforcing filler. The layered structure of nano-montmorillonite effectively blocks the penetration of moisture molecules and synergistically forms a more robust framework structure with the activated rubber powder. Experimental data show that the dynamic stability of this formulation at 60 degrees Celsius is 25% higher than that of conventional materials.

[0057] In Example 3, the flexible sensing network unit employs a non-uniform mesh layout. At the crack edges, where it intersects with the old pavement, the mesh density is compressed to 3 cm, while a 10 cm spacing is maintained in the crack center. This design is based on finite element analysis, which indicates that interfacial debonding often begins at the interface between the repair material and the old asphalt concrete. By densifying the edge sensors, the system can capture interfacial slip signals at the micrometer level.

[0058] In this embodiment, the data acquisition and signal conditioning module integrates a redundant self-diagnostic unit. This unit automatically sends a standard, weak excitation signal to the sensing link every 24 hours to measure the complex impedance of the circuit. If the detected impedance value deviates from the initial calibration value by more than 35%, the self-diagnostic unit immediately determines that it is due to lead wire fatigue fracture caused by long-term high-frequency vibration and generates a specific hardware fault code on the remote cloud management platform. This mechanism effectively avoids false alarms caused by sensor damage, improving the system's technical reliability.

[0059] In Example 3, the self-powered energy harvesting system features optimized power distribution. Considering the characteristics of urban expressways with ample nighttime lighting but small temperature differences, the system increases the proportion of piezoelectric energy harvesting arrays, raising the number of piezoelectric transducers to 36. The captured pulsed current passes through a high-efficiency charge pump converter, injecting the scattered energy into the supercapacitor with a conversion efficiency exceeding 92%. Simultaneously, the microcontroller unit of the edge computing terminal, through load balancing between cores, schedules the most power-intensive wireless data transmission tasks to be executed during periods when the supercapacitor voltage is high.

[0060] When processing large-scale data from urban expressways, the remote cloud management platform employs an edge-cloud collaborative filtering mechanism. Edge computing terminals only upload key parameters after feature extraction, such as strain maxima, waveform width, and the calculated crack health index H. Only when the health index exceeds the warning threshold of 0.6 will the edge computing terminal initiate the full upload of the original waveform data. This data flow control strategy significantly reduces the spectrum occupancy rate of city-level narrowband IoT, ensuring that the system maintains a second-level warning response speed even with tens of thousands of monitoring points operating simultaneously.

[0061] In this embodiment, the protective encapsulation phase of the system is enhanced. The thickness of the modified asphalt anti-cracking tape covering the repair area is increased to 3 mm, with a high-strength fiberglass mesh sandwiched inside. After spreading fine sand, static compaction is performed using a small road roller. This encapsulation structure not only prevents wheel sticking during the initial stage of traffic but also provides a rigid mechanical protective layer for the flexible sensor network unit below, enabling it to withstand the shearing forces of heavy-duty vehicles.

[0062] The self-adhesive rubber asphalt composite layer exhibited excellent stability in its adhesive properties in Example 3. Core sampling of the repaired area after one year of service showed that its interfacial bond strength remained above 1.2 MPa, significantly higher than that of traditional crack sealing materials. This is primarily attributed to the three-dimensional network formed by the styrene-butadiene-styrene block copolymer and activated rubber powder. This network possesses excellent stress relaxation capabilities under long-term alternating loads, absorbing vehicle impact energy through the flexible sliding of molecular chains and preventing secondary cracking caused by stress accumulation at the crack tip.

[0063] In summary, this invention constructs a multi-level, highly reliable highway repair system through scientific optimization of material formulations, deep integration of sensor networks, and the introduction of self-powered and edge computing technologies. This system not only solves the persistent problems of easy detachment and cracking after repair, but also achieves digital and real-time monitoring of the repair status. Each embodiment, tailored to different application scenarios, demonstrates the system's strong environmental adaptability and engineering practical value by adjusting the proportions of material components, sensor density, and data sampling strategies. From microscopic molecular chain design to macroscopic cloud-based big data prediction, this system provides a complete technical solution for the construction of smart highways, significantly improving the operational safety and maintenance efficiency of transportation infrastructure.

[0064] All physical quantities, chemical component proportions, process parameters, and calculation formulas involved in the specific implementation are optimized results derived from extensive laboratory simulation experiments and on-site engineering tests. In practical applications, relevant technical personnel can make reasonable parameter fine-tuning within the technical framework defined in the claims, based on the traffic load level, annual average temperature fluctuation range, and rainfall abundance of specific road sections. All such fine-tuning should fall within the protection scope of this invention. Electrical connections and data interactions between the system modules follow standardized communication protocols, ensuring interoperability and scalability during large-scale deployment.

[0065] The remote cloud management platform also features 3D visualization capabilities. Based on a high-precision road network model, the platform can dynamically display the stress cloud map of cracks across the entire road section. When the strain intensity in a certain area exceeds the material's design allowable stress value, the corresponding road section on the platform interface will change from green to flashing red, and a warning message will be simultaneously pushed to the mobile terminals of maintenance personnel.

[0066] The system also includes a handheld calibration terminal for initial zeroing and gain calibration of the sensor signals at the construction site. The handheld calibration terminal establishes a short-range connection with the edge computing terminal via Bluetooth, reads the raw level of the sensor under no-load conditions, and stores it as a reference bias value, which serves as the physical origin for all subsequent calculations.

[0067] In the preparation process of the self-adhesive rubber asphalt composite layer, the rotation speed of shear emulsification is controlled at 3000 to 5000 rpm, and the duration is not less than 45 minutes. This process parameter ensures that the polymer modifier and activated rubber powder are fully swollen and uniformly dispersed in the base asphalt, eliminating the risk of macroscopic phase separation.

[0068] The system's monitoring frequency has adaptive adjustment characteristics. When the edge computing terminal detects that the ambient temperature is in a freezing or high-temperature period, or when traffic flow increases dramatically, the system automatically increases the sampling frequency to 400 Hz; during periods of mild ambient temperature and low traffic flow, the system automatically switches to a low-power sleep mode, reducing the sampling frequency to 10 Hz, in order to extend the overall service life of the system.

[0069] When repairing wide cracks, i.e. cracks wider than 20 mm, the system incorporates 3 to 5 parts by weight of polypropylene fibers with a length of 12 to 15 mm into the self-adhesive rubber asphalt composite material layer. The fibers limit secondary cracking through bridging and provide additional physical protection for the flexible sensor network unit.

[0070] By developing a self-adhesive rubber asphalt composite material with a specific ratio, and utilizing multiple synergistic modifications of styrene-butadiene-styrene block copolymer, styrene-butadiene rubber, and activated rubber powder, a viscoelastic system with high toughness and high adhesion was constructed. This material exhibits excellent flexibility at low temperatures, effectively overcoming the shortcomings of traditional repair materials that are prone to brittleness. Its bonding strength with the old pavement interface is increased by more than 40% compared with traditional materials, ensuring the long-term service stability of the repaired area under heavy traffic loads.

[0071] By directly integrating flexible sensor network units into the repair material, the repair body has been transformed from a passive filling to an active sensing role. The high degree of matching between the sensor and the asphalt matrix in terms of mechanical parameters eliminates the abrupt change in interface stiffness between traditional embedded sensors and the road structure, enabling the accurate acquisition of microscopic mechanical evolution data deep within cracks. This solves the problem that sensor signals are easily interfered with and cannot reflect the true stress distribution.

[0072] The system integrates a self-powered energy capture system and an edge computing terminal, constructing a completely closed-loop autonomous monitoring ecosystem. Through piezoelectric and thermoelectric dual-energy complementary capture technology, it completely eliminates the dependence of highway field monitoring stations on mains power or frequent battery replacements. Edge computing technology completes feature extraction of massive information at the data source, significantly reducing the bandwidth pressure and power consumption of wireless communication, enabling the system to have a second-level response capability to the risk of sudden deterioration of cracks.

[0073] The systematic framework established by this invention, encompassing materials science, electronic sensing, energy harvesting, and cloud-based big data, provides information support for the entire lifecycle of highway maintenance. The deep learning prediction function of the cloud platform transforms the maintenance model from the existing reactive repair to scientific preventive maintenance, accurately predicting potential structural deterioration risks, thereby rationally allocating maintenance resources, significantly reducing the overall operation and maintenance costs of the highway network, and extending the service life of asphalt pavements.

[0074] At the specific circuit implementation level, the bandpass filter of the data acquisition and signal conditioning module adopts a fourth-order Butterworth architecture to achieve a flat passband gain and steep cutoff characteristics. The 16-bit analog-to-digital converter communicates in full-duplex with the microcontroller unit of the edge computing terminal through a serial peripheral interface. Data verification uses a cyclic redundancy check (CRC) algorithm to ensure zero bit error rate transmission even in environments with strong electromagnetic interference.

[0075] The edge computing terminal's wireless communication module supports low-latency machine-type communication mode in 5G mobile communication technology, and its uplink bandwidth can be dynamically allocated according to the burst data volume. At the instant a structural failure occurs in a crack, the system can push alarm messages to the cloud with millisecond-level latency. The remote cloud management platform adopts a distributed architecture, capable of simultaneously accessing and processing monitoring data from over 100,000 edge nodes. Its data processing layer is based on a streaming computing architecture, enabling real-time spatial correlation analysis of the evolution of cracks at multiple locations.

[0076] Through the detailed description of the above-described implementation methods, it can be seen that this invention has made profound innovations in multiple dimensions, including material composition ratios, sensor structure design, signal processing algorithms, and energy cycling mechanisms. This interdisciplinary technology integration model provides a new engineering paradigm for solving the global maintenance challenge of asphalt road cracking. The system is not only theoretically complete but also demonstrates extremely high stability and reliability in practical engineering applications. In the future, with further reductions in sensor costs and continuous improvements in edge computing power, this system is expected to play a core role in a broader field of traffic infrastructure monitoring, comprehensively improving the safety level of highway traffic.

[0077] The self-adhesive rubber-asphalt composite layer exhibits a microstructure of interpenetrating network structures formed by polymer chains and rubber particles within the asphalt matrix. This structure demonstrates exceptional stress relaxation properties on a macroscopic mechanical level. When the pavement structure cracks and shifts due to temperature or load stress, the composite layer can reduce stress concentration through the relative sliding of its internal molecules, thus preventing abrupt stress changes at the interface between the repair and the old pavement. This mechanical property was verified in real-time using flexible sensor network units. The strain rate curves captured by the sensor network accurately reflect the viscoelastic transformation process within the material, providing the most accurate first-hand data for optimizing material formulations.

[0078] The rectifier and voltage regulator circuit in the self-powered energy harvesting system employs an ultra-low-power integrated chip. This chip can limit the extremely high pulse voltage generated by the piezoelectric transducer and efficiently convert it into a standard 3.3-volt DC voltage, which is then stored in a supercapacitor. In the thermoelectric power generation circuit, through maximum power point tracking technology, the system can adjust the input impedance in real time according to changes in road surface temperature, ensuring that the energy harvesting efficiency remains near the theoretical peak. This refined power management strategy guarantees that the sensor can maintain basic monitoring functions even in environments with extremely low traffic flow.

[0079] In the crack evolution assessment model executed by the edge computing terminal, the extraction of residual strain data employs a differential moving average filtering technique. This technique filters out long-term baseline drift caused by sensor temperature drift, retaining only irreversible plastic deformation caused by vehicle loads. When the residual strain increment continuously exceeds a critical threshold, the algorithm automatically calculates the equivalent area of ​​the debonding region and uses this parameter as an important weighting input for the crack health index H. This assessment method, based on a combination of physical models and statistics, significantly improves the accuracy of pavement distress diagnosis.

[0080] The remote cloud management platform, by incorporating a geospatial information system, enables asset-based management of all repair points within the highway network. Maintenance management departments can visually view the health status of each road segment on an electronic map using color-coding. The platform's built-in decision-making engine automatically generates optimized maintenance path planning suggestions based on predicted disease evolution trends. For example, if a section of highway is predicted to experience large-scale delamination risk within the next three months, the platform will recommend that management departments arrange preventative reinforcement in advance, thereby avoiding the high costs and traffic disruptions associated with major post-repair repairs.

[0081] At the construction site, the system's convenience was fully demonstrated. The high adhesion of the self-adhesive rubber asphalt composite layer allows it to bond firmly to the old pavement without the need for additional primer. The modular design of the sensing unit allows it to be easily unfolded into cracks like a roll of material. The modified asphalt anti-crack tape used in the protective sealing stage has extremely strong shear resistance, effectively resisting the wear of the sweeper brushes. The entire system's construction process has minimal impact on traffic, and the repaired road surface can be reopened to traffic within one hour. This efficient, intelligent, and long-lasting integrated repair and monitoring solution represents the future development direction of smart highway maintenance technology.

Claims

1. A system for repairing and monitoring cracks in asphalt highways integrating self-adhesive rubber asphalt, characterized in that, include: Self-adhesive rubber asphalt composite material layer is used to fill and seal cracks in asphalt roads, achieving stress absorption and flexible connection of pavement structure; The self-adhesive rubber asphalt composite material layer constructs an elastic three-dimensional cross-linked network in the matrix asphalt, forming an interpenetrating network structure of polymer chains and rubber particles in the asphalt matrix in the microstructure, so as to exhibit stress relaxation performance in macroscopic mechanics, and reduce stress concentration through the relative sliding of internal molecules. A flexible sensing network unit is embedded inside the self-adhesive rubber asphalt composite material layer to sense the strain field intensity, displacement vector, and ambient temperature changes in the crack region. The flexible sensing network unit includes a distributed carbon nanotube composite conductive fiber sensor and a fiber Bragg grating sensor. Strain sensing is achieved through the piezoresistive effect of the carbon nanotube composite conductive fiber sensor, and temperature drift compensation is performed using the temperature data of the fiber Bragg grating sensor. The data acquisition and signal conditioning module is electrically connected to the flexible sensor network unit and is used to amplify, filter and perform analog-to-digital conversion on the original sensor signal to generate a digital representation signal. A self-powered energy capture system is deployed inside or on the surface of an asphalt pavement structure to capture environmental mechanical or thermal energy and convert it into electrical energy, providing a continuous energy supply for the data acquisition and signal conditioning module. An edge computing terminal is used to receive the digital characterization signal and perform localized calculations based on a preset crack evolution evaluation model to extract crack propagation rate, fatigue damage degree and material debonding risk index. The remote cloud management platform interacts with the edge computing terminal through a wireless communication link to perform cross-temporal and spatial correlation analysis on monitoring data from multiple locations and to issue maintenance decision instructions.

2. The asphalt highway crack repair and monitoring system integrating self-adhesive rubber asphalt according to claim 1, characterized in that, The self-adhesive rubber asphalt composite material layer is prepared by a shear emulsification process using the following components in parts by weight: 100 parts of base asphalt with a penetration grade of 70 or 90; 12 to 18 parts of a polymer modifier made by mixing styrene-butadiene styrene block copolymer and styrene-butadiene rubber in a mass ratio of 2:1; 5 to 10 parts of tackifying resin including petroleum resin or rosin resin; 20 to 30 parts of activated rubber powder with a mesh size of 60 to 80 mesh and desulfurized and activated; 0.5 to 1.5 parts of anti-aging additives including antioxidants and ultraviolet absorbers; and 2 to 5 parts of nano-reinforcing fillers including nano-silica or montmorillonite. The rotation speed of the shear emulsification process is controlled at 3000 to 5000 rpm for a duration of not less than 45 minutes to ensure the swelling and uniform dispersion of the components in the base asphalt.

3. The asphalt highway crack repair and monitoring system integrating self-adhesive rubber asphalt according to claim 1, characterized in that, The self-adhesive rubber asphalt composite material layer has a penetration of 1 / 50 mm to 1 / 70 mm at 25 degrees Celsius, a softening point of not less than 85 degrees Celsius, and a ductility of not less than 30 cm at 5 degrees Celsius. When the width of the asphalt road crack exceeds 20 mm, the self-adhesive rubber asphalt composite material layer also incorporates 3 to 5 parts by weight of polypropylene fibers with a length of 12 to 15 mm. The bridging effect of the fibers limits the secondary cracking of the crack and provides physical protection for the flexible sensing network unit.

4. The asphalt highway crack repair and monitoring system integrating self-adhesive rubber asphalt according to claim 1, characterized in that, The carbon nanotube composite conductive fiber sensor in the flexible sensing network unit uses thermoplastic polyurethane as the substrate, with a tensile limit of not less than 150% and an elastic modulus matching the modulus of the self-adhesive rubber asphalt composite material layer; the fiber Bragg grating sensor is encapsulated in a carbon fiber composite tube and arranged in the middle region of the asphalt road crack; the carbon nanotube composite conductive fiber sensor is arranged in a mesh structure with a grid spacing set to 5 cm or 10 cm according to the road technical grade, so as to achieve full coverage monitoring of stress distribution in the crack area.

5. The asphalt highway crack repair and monitoring system integrating self-adhesive rubber asphalt according to claim 1, characterized in that, The data acquisition and signal conditioning module includes a preamplifier circuit, a bandpass filter, a 16-bit high-precision analog-to-digital converter, and a self-diagnostic unit. The input impedance of the preamplifier circuit is not less than 10 megohms. The cutoff frequency of the bandpass filter is set to 0.5 Hz to 50 Hz according to the vehicle load characteristics. The sampling frequency of the digital characterization signal is not less than 200 Hz. The self-diagnostic unit is used to periodically detect the impedance status of the sensor link. When the impedance value deviates from the initial calibration value by more than 30%, it is automatically identified as sensor damage or lead wire breakage, and a hardware fault alarm is generated on the remote cloud management platform.

6. The asphalt highway crack repair and monitoring system integrating self-adhesive rubber asphalt according to claim 1, characterized in that, The self-powered energy harvesting system includes a piezoelectric energy harvesting array, a thermoelectric power generation component, and a solid-state supercapacitor for storing the harvested electrical energy. The piezoelectric energy harvesting array consists of multiple sets of lead zirconate titanate piezoelectric ceramic transducers, which are buried 5 to 8 centimeters below the wheel tracks on the road surface. The thermoelectric power generation component utilizes the temperature gradient between the asphalt pavement surface and the base layer to generate direct current. The solid-state supercapacitor adopts a parallel redundant design with a total capacitance of not less than 50 farads, and is equipped with a charge and discharge management circuit with overcharge protection and over-discharge protection functions. The static power consumption of the charge and discharge management circuit is controlled below 10 microamps.

7. The asphalt highway crack repair and monitoring system integrating self-adhesive rubber asphalt according to claim 1, characterized in that, The edge computing terminal integrates a wireless communication module and a microcontroller unit with a dual-core architecture (first core and second core). The first core is responsible for high-frequency data acquisition and real-time processing, while the second core is responsible for maintaining the wireless communication protocol stack and scheduling low-frequency tasks. The two cores exchange data through shared memory. The wireless communication module supports narrowband IoT communication protocols or 5G mobile communication technology. The edge computing terminal is also equipped with a handheld calibration terminal, which connects to the edge computing terminal via Bluetooth. This handheld calibration terminal is used to read the raw voltage levels of the flexible sensor network unit under no-load conditions and store them as a reference bias value, serving as the physical origin for subsequent calculations.

8. The asphalt highway crack repair and monitoring system integrating self-adhesive rubber asphalt according to claim 1, characterized in that, The crack evolution assessment model executed by the edge computing terminal includes the following steps: Step 1, acquiring the real-time strain sequence fed back by the flexible sensing network unit, and using the temperature data of the fiber Bragg grating sensor for temperature drift compensation; Step 2, calculating the mean change rate of the peak strain per unit time, and determining that the crack has entered the accelerated propagation stage when the change rate exceeds a preset threshold of 10% for three consecutive monitoring cycles; Step 3, extracting the residual strain data after vehicle load, and determining that the self-adhesive rubber asphalt composite material layer and the crack interface have undergone local debonding when the residual strain shows a step-like growth trend; Step 4, generating a crack health index divided into four levels based on the weighted calculation of the ratio of the maximum peak strain to the design limit allowable strain, the ratio of the residual strain increment to the cumulative total strain, and the ratio of the current crack propagation rate to the reference rate index.

9. The asphalt highway crack repair and monitoring system integrating self-adhesive rubber asphalt according to claim 1, characterized in that, The remote cloud management platform includes a spatial geographic information system database and a deep learning prediction module. The spatial geographic information system database is used to store coordinate information, construction records, material batches, and historical monitoring data for crack repair. The deep learning prediction module is based on a long short-term memory network structure, takes historical traffic flow, rainfall, extreme temperature changes, and real-time monitoring indicators as input, and outputs a pavement structure defect evolution prediction report. The remote cloud management platform also has a three-dimensional visualization display function, dynamically presenting the stress cloud map of cracks in the entire road section based on a high-precision road network model, and pushing early warning information through a red flashing signal when the strain intensity exceeds the design allowable stress value of the material.

10. The asphalt highway crack repair and monitoring system integrating self-adhesive rubber asphalt according to claim 1, characterized in that, The monitoring frequency of the monitoring system has an adaptive adjustment characteristic: when the edge computing terminal detects that the ambient temperature is in a freezing period, a high temperature period, or a surge in traffic flow, the system automatically increases the sampling frequency to 400 Hz; during periods of mild ambient temperature and low traffic flow, the system automatically switches to a low-power sleep mode and reduces the sampling frequency to 10 Hz. The construction process of the monitoring system includes: pre-treating the cracks with high-pressure air and a hot spray gun to ensure that the dryness is not less than 95%; and then tensioning and fixing the flexible sensor network unit after applying a self-adhesive rubber asphalt leveling layer with a thickness of 1 mm to 2 mm to the bottom of the crack. The crack is filled in layers using a material with a melting temperature between 160 and 180 degrees Celsius, with each layer controlled to be less than 10 millimeters thick. Modified asphalt anti-crack tape is then applied to the surface of the repaired area, and fine sand is sprinkled on top to complete the protective sealing.

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